Modern mobile devices have evolved to include image capturing capabilities through the use of cameras as well as high speed processors. Taking advantage of such features, some mobile devices have provided text recognition capability to recognize text from a captured image of a document. Users of such mobile devices have extended the use of such capabilities to objects beyond paper documents such as credit cards, ID cards, etc. to recognize text information in the objects.
Conventional text recognition methods in mobile devices have generally recognized text blocks in an object based on a single object image. For example, mobile devices with conventional text recognition capabilities typically allow a user to capture a single image of an object. Text blocks in the object image are then recognized by processing the object image.
However, such conventional text recognition methods based on a single object image often suffer from inaccuracies in recognizing the characters in the text blocks due to varying conditions under which the image may be captured. For example, an image of an object may be captured under less than optimum lighting conditions such as light reflection, poor lighting, etc., which may degrade the quality of the captured image. Further, in a mobile device setting, some portions of the image may be captured out of focus or may suffer from blurring due to unwanted motion of the mobile device in the user's control.
Thus, there is a need for a method and system that allow recognition of text blocks in objects more accurately in various conditions under which the object images are captured.